Artificial Intelligence (AI) in Automotive Training Course

This live, instructor-led training in the Netherlands—conducted online or on-site—is designed for advanced robotics engineers and AI researchers seeking to implement sophisticated path planning algorithms to improve autonomous vehicle performance.

By the end of this training, participants will have the ability to:

• Understand the theoretical foundations of advanced path planning algorithms.
• Implement algorithms such as RRT*, A*, and D* for real-time navigation.
• Optimize path planning for obstacle avoidance and dynamic environments.
• Integrate path planning algorithms with sensor data for enhanced accuracy.
• Evaluate the performance of various algorithms in practical scenarios.

This instructor-led live training in the Netherlands (available online or on-site) is intended for advanced data scientists, AI specialists, and automotive AI developers who aim to build, train, and optimise AI models for autonomous driving applications.

By the end of this training, participants will be able to:

• Understand the core principles of AI and deep learning as they apply to autonomous vehicles.
• Implement computer vision techniques for real-time object detection and lane following.
• Utilise reinforcement learning to support decision-making in self-driving systems.
• Integrate sensor fusion techniques to enhance perception and navigation.
• Construct deep learning models to predict and analyse driving scenarios.

This live, instructor-led course in the Netherlands focuses on the design and development of automotive software utilizing both AUTOSAR Classic and Adaptive platforms. Learners will acquire practical skills to configure components, integrate ADAS software, and implement safety best practices using industry-standard tools.

This instructor-led live training in the Netherlands (online or on-site) is primarily aimed at engineers who wish to use Autosar to design automotive components.

By the end of this training, participants will be able to:

• Install and configure Autosar.
• Set up a workflow.
• Navigate smoothly in the Autosar environment.
• Work efficiently.

This instructor-led, live training (online or onsite) is designed for intermediate-level embedded software developers and automotive engineers who want to utilise the AUTOSAR Classic Platform to develop, integrate, and test standardised software components for electronic control units (ECUs).

Upon completion of this training, participants will be able to:

Install and configure AUTOSAR development tools (e.g., DaVinci Developer, EB Tresos, or ETAS ISOLAR-A/B).

Grasp the AUTOSAR layered architecture and basic software modules (BSW).

Design and implement AUTOSAR OS and the communication stack (COM stack).

Use CANoe or similar tools for simulation, testing, and diagnostics within an AUTOSAR environment.

This instructor-led, live training (available online or onsite) is designed for intermediate-level embedded software developers or automotive engineers seeking to understand and configure AUTOSAR OS (based on OSEK/VDX) and the COM Stack. The course focuses on enabling reliable task scheduling and communication within automotive ECUs.

Upon completion of this training, participants will be able to:

• Gain insight into the AUTOSAR OS architecture and its scheduling policies
• Implement and manage tasks, events, alarms, and counters
• Describe and configure COM Stack layers, including PDUR and communication services
• Explain protocol stacks (CAN, LIN, FlexRay, Ethernet) and AUTOSAR's interaction with them
• Configure OS and COM modules using industry-standard tools such as Vector DaVinci or ETAS ISOLAR
• Simulate and validate task and communication flows in an AUTOSAR-based ECU

This instructor-led, live training in the Netherlands (online or onsite) is aimed at advanced-level safety engineers and automotive safety professionals who wish to develop comprehensive safety strategies for autonomous vehicles, including hazard analysis, functional safety assessments, and compliance with international standards.

By the end of this training, participants will be able to:

• Identify and assess safety risks associated with autonomous driving systems.
• Conduct hazard analysis and risk assessment using industry standards.
• Implement safety validation and verification methods for AV systems.
• Apply functional safety standards, such as ISO 26262 and SOTIF.
• Develop risk mitigation strategies for AV safety challenges.

This instructor-led, live training in the Netherlands (online or onsite) targets intermediate-level AI developers and computer vision engineers who aim to develop robust vision systems for autonomous driving applications. <\/p>

Upon completing this training, participants will be capable of: <\/p>

• Grasping the fundamental concepts of computer vision in autonomous vehicles. <\/li>
• Implementing algorithms for object detection, lane detection, and semantic segmentation. <\/li>
• Integrating vision systems with other autonomous vehicle subsystems. <\/li>
• Applying deep learning techniques for advanced perception tasks. <\/li>
• Evaluating the performance of computer vision models in real-world scenarios. <\/li> <\/ul>

This instructor-led, live training in the Netherlands (online or on-site) is aimed at experienced professionals who wish to deepen their knowledge of computer vision and explore TensorFlow's capabilities for developing sophisticated vision models using Google Colab.

By the end of this training, participants will be able to:

• Construct and train convolutional neural networks (CNNs) using TensorFlow.
• Leverage Google Colab for scalable and efficient cloud-based model development.
• Implement image preprocessing techniques for computer vision tasks.
• Deploy computer vision models for real-world applications.
• Use transfer learning to enhance the performance of CNN models.
• Visualize and interpret the results of image classification models.

This instructor-led live training in the Netherlands (online or onsite) is designed for intermediate-level developers, data scientists, and AI practitioners who aim to utilize TensorFlow Lite for Edge AI applications.

By the conclusion of this training, participants will be able to:

• Understand the core principles of TensorFlow Lite and its function within Edge AI.
• Construct and refine AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on a variety of edge devices.
• Employ tools and techniques for model conversion and optimization.
• Build practical Edge AI applications leveraging TensorFlow Lite.

This instructor-led, live training in the Netherlands (online or onsite) is aimed at beginner-level professionals who wish to explore the ethical dilemmas and legal frameworks surrounding autonomous vehicles.

By the end of this training, participants will be able to:

• Understand the ethical implications of AI-driven decision-making in autonomous vehicles.
• Analyze global legal frameworks and policies regulating self-driving cars.
• Examine liability and accountability in the event of autonomous vehicle accidents.
• Evaluate the balance between innovation and public safety in autonomous driving laws.
• Discuss real-world case studies involving ethical dilemmas and legal disputes.

This instructor-led, live training in the Netherlands (online or onsite) is aimed at beginner-level professionals and enthusiasts who wish to understand the fundamental concepts, technologies, and applications of autonomous vehicles.

By the end of this training, participants will be able to:

• Understand the key components and working principles of autonomous vehicles.
• Explore the role of AI, sensors, and real-time data processing in self-driving systems.
• Analyze different levels of vehicle autonomy and their real-world applications.
• Examine the ethical, legal, and regulatory aspects of autonomous mobility.
• Gain hands-on exposure to autonomous vehicle simulations.

This instructor-led, live training in the Netherlands (online or onsite) is aimed at advanced-level sensor fusion specialists and AI engineers who wish to develop multi-sensor fusion algorithms and optimize real-time navigation in autonomous systems.

By the end of this training, participants will be able to:

• Understand the fundamentals and challenges of multi-sensor data fusion.
• Implement sensor fusion algorithms for real-time autonomous navigation.
• Integrate data from LiDAR, cameras, and RADAR for perception enhancement.
• Analyze and evaluate fusion system performance under various conditions.
• Develop practical solutions for sensor noise reduction and data alignment.

This instructor-led live training in the Netherlands (online or onsite) is tailored for intermediate-level engineers, automotive professionals, and IoT specialists aiming to understand the role of sensors in self-driving cars. Topics include LiDAR, radar, cameras, and sensor fusion techniques.

Upon completion of this training, participants will be able to:

• Identify the various types of sensors utilized in autonomous vehicles.
• Analyze sensor data to support real-time vehicle perception and decision-making processes.
• Apply sensor fusion techniques to enhance vehicle accuracy and safety.
• Optimize sensor placement and calibration to improve autonomous driving performance.

This instructor-led live training in the Netherlands (online or onsite) is designed for intermediate network engineers and automotive IoT developers who wish to understand and implement V2X communication technologies for autonomous vehicles.

By the end of this training, participants will be able to:

• Grasp the core principles of V2X communication.
• Evaluate V2V, V2I, V2P, and V2N communication models.
• Deploy V2X protocols including DSRC and C-V2X.
• Create simulations for connected vehicle ecosystems.
• Tackle cybersecurity and privacy issues within V2X networks.